"""
基于量子计算的资产组合优化策略
策略特点：
1. 使用量子退火算法求解组合优化问题
2. 支持大规模资产组合的快速优化
3. 实现经典-量子混合计算架构
"""

import numpy as np
import dimod
from dwave.system import LeapHybridSampler
from qiskit import QuantumCircuit, Aer, execute
from qiskit.algorithms import QAOA
from qiskit_optimization import QuadraticProgram
from qiskit_optimization.algorithms import MinimumEigenOptimizer

class QuantumPortfolioOptimizer:
    def __init__(self, asset_returns, cov_matrix):
        """
        asset_returns: 资产预期收益率
        cov_matrix: 资产协方差矩阵
        """
        self.returns = asset_returns
        self.cov_matrix = cov_matrix
        self.num_assets = len(asset_returns)
        
    def classical_qp_formulation(self, risk_aversion=0.5):
        """构建经典二次规划问题"""
        qp = QuadraticProgram()
        
        # 添加变量
        for i in range(self.num_assets):
            qp.binary_var(name=f'x_{i}')  # 二元变量表示是否持有
        
        # 目标函数: 最大化收益 - 风险厌恶*风险
        linear_terms = {f'x_{i}': -self.returns[i] for i in range(self.num_assets)}
        quadratic_terms = {(f'x_{i}', f'x_{j}'): risk_aversion*self.cov_matrix[i,j] 
                         for i in range(self.num_assets) 
                         for j in range(self.num_assets)}
        
        qp.minimize(linear=linear_terms, quadratic=quadratic_terms)
        
        # 约束条件
        qp.linear_constraint(linear={f'x_{i}':1 for i in range(self.num_assets)}, 
                           sense='==', 
                           rhs=10)  # 持有10种资产
        
        return qp
        
    def solve_with_qaoa(self, risk_aversion=0.5):
        """使用QAOA算法求解"""
        qp = self.classical_qp_formulation(risk_aversion)
        
        # 设置QAOA
        qaoa = QAOA(quantum_instance=Aer.get_backend('qasm_simulator'))
        optimizer = MinimumEigenOptimizer(qaoa)
        
        # 求解
        result = optimizer.solve(qp)
        
        return [result.variables[i].value for i in range(self.num_assets)]
        
    def solve_with_quantum_annealing(self, risk_aversion=0.5):
        """使用量子退火求解"""
        qp = self.classical_qp_formulation(risk_aversion)
        
        # 转换为QUBO
        qubo = qp.to_qubo()
        bqm = dimod.BQM.from_qubo(qubo[0], offset=qubo[1])
        
        # 使用D-Wave求解器
        sampler = LeapHybridSampler()
        sampleset = sampler.sample(bqm)
        
        # 获取最优解
        best_sample = sampleset.first.sample
        
        return [best_sample[f'x_{i}'] for i in range(self.num_assets)]
        
    def hybrid_solution(self, risk_aversion=0.5):
        """混合经典-量子解决方案"""
        # 经典预处理缩小解空间
        reduced_assets = self._classical_prescreen()
        
        # 量子优化核心资产
        core_solution = self.solve_with_qaoa(risk_aversion)
        
        # 组合最终结果
        return self._combine_solutions(reduced_assets, core_solution)
        
    def _classical_prescreen(self, top_n=20):
        """经典方法预筛选资产"""
        sharpe_ratios = self.returns / np.diag(self.cov_matrix)
        return np.argsort(sharpe_ratios)[-top_n:]
        
    def _combine_solutions(self, reduced_assets, core_solution):
        """组合经典和量子解决方案"""
        full_solution = [0] * self.num_assets
        for i, asset in enumerate(reduced_assets):
            full_solution[asset] = core_solution[i]
        return full_solution

if __name__ == '__main__':
    # 示例用法
    np.random.seed(42)
    num_assets = 50
    returns = np.random.uniform(0.01, 0.15, num_assets)
    cov = np.random.uniform(0.05, 0.3, (num_assets, num_assets))
    cov = (cov + cov.T) / 2  # 对称化协方差矩阵
    np.fill_diagonal(cov, 0.1)  # 设置方差
    
    optimizer = QuantumPortfolioOptimizer(returns, cov)
    
    print("QAOA解决方案:", optimizer.solve_with_qaoa())
    print("量子退火解决方案:", optimizer.solve_with_quantum_annealing())
    print("混合解决方案:", optimizer.hybrid_solution())